第1 -第10个机器学习练习

￡神魔★判官ぃ 2024-03-24 09:57 124阅读 0赞

1.导入scikit-learn库

  1. importsklearn

2.加载数据集

  1. fromsklearn.datasetsimportload_iris
  2. iris=load_iris()
  3. X=iris.data
  4. y=iris.target

3.划分数据集为训练集和测试集

  1. fromsklearn.model_selectionimporttrain_test_split
  2. X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)

4.标准化特征值

  1. fromsklearn.preprocessingimportStandardScaler
  2. scaler=StandardScaler()
  3. X_train_scaled=scaler.fit_transform(X_train)
  4. X_test_scaled=scaler.transform(X_test)

5.训练线性回归模型

  1. fromsklearn.linear_modelimportLinearRegression
  2. regressor=LinearRegression()
  3. regressor.fit(X_train,y_train)

6.使用k近邻算法分类

  1. fromsklearn.neighborsimportKNeighborsClassifier
  2. classifier=KNeighborsClassifier(n_neighbors=3)
  3. classifier.fit(X_train,y_train)

7.计算决策树分类器的准确率

  1. fromsklearn.treeimportDecisionTreeClassifier
  2. classifier=DecisionTreeClassifier(random_state=42)
  3. classifier.fit(X_train,y_train)
  4. score=classifier.score(X_test,y_test)
  5. print("Accuracy:",score)

8.计算朴素贝叶斯分类器的准确率

  1. fromsklearn.naive_bayesimportGaussianNB
  2. classifier=GaussianNB()
  3. classifier.fit(X_train,y_train)
  4. score=classifier.score(X_test,y_test)
  5. print("Accuracy:",score)

9.计算支持向量机分类器的准确率

  1. fromsklearn.svmimportSVC
  2. classifier=SVC(random_state=42)
  3. classifier.fit(X_train,y_train)
  4. score=classifier.score(X_test,y_test)
  5. print("Accuracy:",score)

10.训练随机森林模型

  1. fromsklearn.ensembleimportRandomForestClassifier
  2. classifier=RandomForestClassifier(n_estimators=100,random_state=42)
  3. classifier.fit(X_train,y_train)

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